Bioinformatics Analysis Reveals Genes Involved in the Pathogenesis of Ameloblastoma and Keratocystic Odontogenic Tumor.

Pathogenesis of odontogenic tumors is not well known. It is important to identify genetic deregulations and molecular alterations. This study aimed to investigate, through bioinformatic analysis, the possible genes involved in the pathogenesis of ameloblastoma (AM) and keratocystic odontogenic tumor (KCOT). Genes involved in the pathogenesis of AM and KCOT were identified in GeneCards. Gene list was expanded, and the gene interactions network was mapped using the STRING software. "Weighted number of links" (WNL) was calculated to identify "leader genes" (highest WNL). Genes were ranked by K-means method and Kruskal-Wallis test was used (P<0.001). Total interactions score (TIS) was also calculated using all interaction data generated by the STRING database, in order to achieve global connectivity for each gene. The topological and ontological analyses were performed using Cytoscape software and BinGO plugin. Literature review data was used to corroborate the bioinformatics data. CDK1 was identified as leader gene for AM. In KCOT group, results show PCNA and TP53. Both tumors exhibit a power law behavior. Our topological analysis suggested leader genes possibly important in the pathogenesis of AM and KCOT, by clustering coefficient calculated for both odontogenic tumors (0.028 for AM, zero for KCOT). The results obtained in the scatter diagram suggest an important relationship of these genes with the molecular processes involved in AM and KCOT. Ontological analysis for both AM and KCOT demonstrated different mechanisms. Bioinformatics analyzes were confirmed through literature review. These results may suggest the involvement of promising genes for a better understanding of the pathogenesis of AM and KCOT.

dontogenic tumors consist of a heterogeneous group of lesions that originate from the tissue that forms the teeth (1). These tumors affect individuals in different age groups, involving mandibular and maxillary region, with central or peripheral location. Some lesions are asymptomatic and are discovered by chance through routine radiographs. Additionally, odontogenic tumors could promote the local expansion or facial swelling (2,3). Pathogenesis of odontogenic tumors is not well known. Several studies were performed to identify genetic deregulations and molecular alterations in an attempt to explain the mechanisms of oncogenesis, cytodifferentiation, and tumor progression (3,4).

Ameloblastoma (AM) is a benign tumor
originating in the odontogenic epithelium without ectomesenchyme, affecting the maxillo-mandibular complex (5). It is an asymptomatic lesion, and it presents locally invasive behavior, and higher recurrence rates (6). The differential diagnosis includes a variety of odontogenic cysts and tumors, particularly keratocyst odontogenic tumor and myxoma, non-odontogenic tumors and cysts, as central giant cell lesions and fibro-osseous lesions (7,8).
The keratocystic odontogenic tumor (KCOT), according to the most recent classification of tumors of the head and neck of the World Health Organization (WHO), has been categorized as benign neoplasm derived from odontogenic epithelium. The great clinical relevance of KCOT is related to aggressive clinical behavior, high recurrence and proliferation rate (9,10). However, there are still disagreements, questioning whether this odontogenic lesion indeed is a neoplasm or a cyst of odontogenic nature (11). Some studies have sought to understand these aspects through molecular investigations (11,12).
Despite efforts focused on understanding the pathogenesis of odontogenic tumors, little is known about the real influence of molecular pathways and gene deregulations in these tumors. Silico approaches, such as bioinformatic analysis, have been performed to investigate signaling pathways, protein interactions, microRNA prediction models, and gene expression to obtain the best understanding of pathological mechanisms of diseases (13). The computational method is an important tool to understand molecular aspects of oral pathology and medicine (14)(15)(16).
This study aimed to investigate the differential involvement of protein-coding genes in the pathogenesis of AM and KCOT, through bioinformatics analysis.

Bioinformatics and biological systems analysis
Initially, key genes involved in the pathogenesis of AM and KCOT were identified by searching the GeneCards database (17). The gene nomenclature adopted was defined by Human Genome Organization (HUGO). The keywords, chosen according to Medical Subject Headings (MeSH), were "ameloblastoma and gene expression" and "keratocystic odontogenic tumor and gene expression".
After this step, a list of potential "candidate genes" related to AM and KCOT was generated to each tumor. Then, this gene list was expanded using the web-available software STRING (version 9.1) (14), mapping the interaction network between these protein-coding genes. Direct and indirect gene interactions were considered with a high degree of confidence (above 0.9, range 0-0.99) (14). With this process, new genes linked to AM and KCOT could be identified. For every gene interaction identified, we summed the interaction score of each gene, generating a combined association score. This score was adjusted, multiplying it by 1,000 (14), to obtain a single value called weighted number of links (WNL). The genes that showed the largest WNL values were named "leader genes" (14). Total interaction score (TIS) was also calculated using all O interaction data generated by the STRING database to achieve global connectivity for each gene involved in the process (14). The value of WNL/TIS ratio represents the most influential genes in the network (specificity score). Genes with no link (orphan genes) were excluded from this analysis.
Genes were ranked according to this parameter in clusters, by the clustering method Kmeans. The number of clusters was calculated using the following equation: Cluster number =TETO (LOG(CONT.NÚM(N);2);1). The number of clusters was obtained when mathematical convergence was achieved. To evaluate the differences among various classes based on WNL, Kruskal-Wallis test was used. Statistical significance was set at a pvalue <0.001. Interacting genes were classified as up-regulated or down-regulated, as previously described (14), to each type of odontogenic tumor, AM or KCOT. Complementary analysis of biological systems was performed by topological and ontological analysis. The first was carried out with Cytoscape software (18), and ontological analysis was performed with BinGO plugin (14).  Data was derived from STRING (level of confidence > 0.9). Down-regulation is a red bar and up-regulation is a green arrow. Yellow circle represents that the directionality of the interaction is known, but it is not known whether it results from the interaction (e.g., if it is up-or down regulated). Black circle at both ends means some kind of interaction between the two proteins, but the directionality is not known. In deep blue: binding; in blue: phenotype; in indigo blue: catalysis; in violet: post-translation; in black: reaction; and in yellow: expression.   have a very high degree. In our case, we saw that few genes showed a large number of connections, whereas most of the genes showed few links. In this case, the high degree nodes are leader genes.

Topological analysis
A clustering coefficient was used to measure the degree of cohesion between the groups of genes. This numeric variable indicates the extent to which a gene is integrated into a given group.
Clustering coefficient was close to zero (0.028) for AM, and zero for KCOT, demonstrating the importance of leader genes in connection between vertices and their neighbors (Table 1). When a gene appears above the regression line and very close to the Y axis, it means that it has a high specificity (WNL) and less global connectivity (TIS) suggesting that it is a leader gene. Figure 4a and 4b show the disease-related connectivities (WNL) versus the global connectivities (TIS). The WNL/TIS ratio indicated that the leader genes from AM (CDK) and KCOT (PCNA and TP53) were influential genes in the interaction networks.   Table 2.  In this study, some genes with an important potential role in the pathogenesis of AM and KCOT were identified. Even with the limitations of any theoretical study, these preliminary results may suggest the involvement of promising genes for a better understanding of these odontogenic tumors.

Acknowledgement
This study was supported by grants from